class_docstr: A fit of the LiFE model to diffusion data
docstr: Predict the signal
  
  Parameters
  ----------
  gtab : GradientTable
      Default: use self.gtab
  S0 : float or array
      The non-diffusion-weighted signal in the voxels for which a
      prediction is made. Default: use self.b0_signal
  
  Returns
  -------
  prediction : ndarray of shape (voxels, bvecs)
      An array with a prediction of the signal in each voxel/direction
docstr_tok: ["Predict","the","signal","Parameters","--","--","--","--","--","gtab",":","GradientTable","Default",":","use","self.gtab","S0",":","float","or","array","The","non-diffusion-weighted","signal","in","the","voxels","for","which","a","prediction","is","made",".","Default",":","use","self.b0_signal","Returns","--","--","--","-","prediction",":","ndarray","of","shape","(","voxels",",","bvecs",")","An","array","with","a","prediction","of","the","signal","in","each","voxel\/direction"]
code: def predict(self, gtab=None, S0=None):
      """
      Predict the signal
  
      Parameters
      ----------
      gtab : GradientTable
          Default: use self.gtab
      S0 : float or array
          The non-diffusion-weighted signal in the voxels for which a
          prediction is made. Default: use self.b0_signal
  
      Returns
      -------
      prediction : ndarray of shape (voxels, bvecs)
          An array with a prediction of the signal in each voxel/direction
      """
      # We generate the prediction and in each voxel, we add the
      # offset, according to the isotropic part of the signal, which was
      # removed prior to fitting:
      if gtab is None:
          _matrix = self.life_matrix
          gtab = self.model.gtab
      else:
          _model = FiberModel(gtab)
          _matrix, _ = _model.setup(
              self.streamline, self.affine, self.evals
          )
  
      pred_weighted = np.reshape(
          opt.spdot(_matrix, self.beta),
          (self.vox_coords.shape[0], np.sum(~gtab.b0s_mask)),
      )
  
      pred = np.empty((self.vox_coords.shape[0], gtab.bvals.shape[0]))
      if S0 is None:
          S0 = self.b0_signal
  
      pred[..., gtab.b0s_mask] = S0[:, None]
      pred[..., ~gtab.b0s_mask] = (
          pred_weighted + self.mean_signal[:, None]
      ) * S0[:, None]
  
      return pred
  
imports: import numpy
  import scipy.sparse
  import scipy.linalg
  from dipy.reconst.base import ReconstModel, ReconstFit
  from dipy.tracking.utils import unique_rows
  from dipy.tracking.streamline import transform_streamlines
  from dipy.tracking.vox2track import _voxel2streamline
  import dipy.data
  import dipy.core.optimize
label_desc: Use image node_id 2 for calling the FiberFit obj's underlying member method code with example usage: obj.predict(gtab, S0) and returns: pred